import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from urllib.request import urlopen
import json
import requests
import re
import math
import warnings
warnings.filterwarnings('ignore')

confirmed_color = 'navy'
recovered_color = 'green'
death_color = 'indianred'
active_color = 'purple'

df1 = "https://api.covid19india.org/state_district_wise.json"
df2 = "https://api.covid19india.org/data.json"

def getting_data(url):
    response = requests.get(url)
    data = response.content.decode('utf-8')
    return data

df_state = json.loads(getting_data(df1))
df = json.loads(getting_data(df2))

lis = []
state_names = df_state.keys()
for state in state_names:
    district_names = df_state[state]['districtData'].keys() #Districts of Current State
    for district in district_names:
        temp = df_state[state]['districtData'][district]
        var_lis = [state,district,temp.get('confirmed'),temp.get('recovered'),
                   temp.get('active'),temp.get('deceased')]
        lis.append(var_lis)
    district_wise = pd.DataFrame(lis,columns=['State/UT','District','Confirmed',
                                              'Recovered','Active','Death'])
district_wise.head()

	State/UT	District	Confirmed	Recovered	Active	Death
0	State Unassigned	Unassigned	0	0	0	0
1	Andaman and Nicobar Islands	Nicobars	0	0	0	0
2	Andaman and Nicobar Islands	North and Middle Andaman	1	1	0	0
3	Andaman and Nicobar Islands	South Andaman	51	32	19	0
4	Andaman and Nicobar Islands	Unknown	7496	7380	-13	129

temp = [[i['state'],i['confirmed'],i['recovered'],i['active'],i['deaths'],
         i['lastupdatedtime']] for i in df['statewise']]
statewise_total = pd.DataFrame(temp,columns=['State/UT','Confirmed','Recovered',
                                          'Active','Death','LastUpdateTime'])
statewise_total.head()

	State/UT	Confirmed	Recovered	Active	Death	LastUpdateTime
0	Total	32249900	31441260	363849	432112	13/08/2021 23:27:22
1	Andaman and Nicobar Islands	7549	7419	1	129	13/08/2021 23:27:22
2	Andhra Pradesh	1994606	1963728	17218	13660	13/08/2021 23:27:22
3	Arunachal Pradesh	51513	49425	1836	252	13/08/2021 23:27:22
4	Assam	580657	566101	7707	5502	13/08/2021 23:27:22

statewise_total['Confirmed']=statewise_total['Confirmed'].astype('int')
statewise_total['Recovered']=statewise_total['Recovered'].astype('int')
statewise_total['Active']=statewise_total['Active'].astype('int')
statewise_total['Death']=statewise_total['Death'].astype('int')
statewise_total['RecoveryRate%'] = round(statewise_total['Recovered']/statewise_total['Confirmed']*100,2)
statewise_total['MortalityRate%'] = round(statewise_total['Death']/statewise_total['Confirmed']*100,2)
statewise_total['Active/100 Confirmed'] = round(statewise_total['Active']/statewise_total['Confirmed']*100,2)
for i,y in enumerate(statewise_total['LastUpdateTime']):
    statewise_total['LastUpdateTime'][i] = pd.to_datetime(y.split(' ')[0])
statewise_total['LastUpdateTime'] = pd.to_datetime(statewise_total['LastUpdateTime'])
statewise_total.head()

	State/UT	Confirmed	Recovered	Active	Death	LastUpdateTime	RecoveryRate%	MortalityRate%	Active/100 Confirmed
0	Total	32249900	31441260	363849	432112	2021-08-13	97.49	1.34	1.13
1	Andaman and Nicobar Islands	7549	7419	1	129	2021-08-13	98.28	1.71	0.01
2	Andhra Pradesh	1994606	1963728	17218	13660	2021-08-13	98.45	0.68	0.86
3	Arunachal Pradesh	51513	49425	1836	252	2021-08-13	95.95	0.49	3.56
4	Assam	580657	566101	7707	5502	2021-08-13	97.49	0.95	1.33

timeseries = [list(i.values()) for i in df['cases_time_series']]
timeseries = pd.DataFrame(timeseries,columns=df['cases_time_series'][0].keys())
timeseries.head()

	dailyconfirmed	dailydeceased	dailyrecovered	date	dateymd	totalconfirmed	totaldeceased	totalrecovered
0	1	0	0	30 January 2020	2020-01-30	1	0	0
1	0	0	0	31 January 2020	2020-01-31	1	0	0
2	0	0	0	1 February 2020	2020-02-01	1	0	0
3	1	0	0	2 February 2020	2020-02-02	2	0	0
4	1	0	0	3 February 2020	2020-02-03	3	0	0

timeseries['dailyconfirmed'] = timeseries['dailyconfirmed'].astype('int')
timeseries['dailydeceased'] = timeseries['dailydeceased'].astype('int')
timeseries['dailyrecovered'] = timeseries['dailyrecovered'].astype('int')
timeseries['totalconfirmed'] = timeseries['totalconfirmed'].astype('int')
timeseries['totaldeceased'] = timeseries['totaldeceased'].astype('int')
timeseries['totalrecovered'] = timeseries['totalrecovered'].astype('int')
timeseries['7dyMnConfirmed'] = timeseries.totalconfirmed.rolling(7).mean().fillna(0).astype(int)
timeseries['7dyMnRecovered'] = timeseries.totalrecovered.rolling(7).mean().fillna(0).astype(int)
timeseries['7dyMnDeceased'] = timeseries.totaldeceased.rolling(7).mean().fillna(0).astype(int)
timeseries.head(10)

	dailyconfirmed	dailydeceased	dailyrecovered	date	dateymd	totalconfirmed	totaldeceased	totalrecovered	7dyMnConfirmed	7dyMnRecovered	7dyMnDeceased
0	1	0	0	30 January 2020	2020-01-30	1	0	0	0	0	0
1	0	0	0	31 January 2020	2020-01-31	1	0	0	0	0	0
2	0	0	0	1 February 2020	2020-02-01	1	0	0	0	0	0
3	1	0	0	2 February 2020	2020-02-02	2	0	0	0	0	0
4	1	0	0	3 February 2020	2020-02-03	3	0	0	0	0	0
5	0	0	0	4 February 2020	2020-02-04	3	0	0	0	0	0
6	0	0	0	5 February 2020	2020-02-05	3	0	0	2	0	0
7	0	0	0	6 February 2020	2020-02-06	3	0	0	2	0	0
8	0	0	0	7 February 2020	2020-02-07	3	0	0	2	0	0
9	0	0	0	8 February 2020	2020-02-08	3	0	0	2	0	0

fig = go.Figure(go.Funnel(
    x = [timeseries['totalconfirmed'].iloc[-1],timeseries['totalrecovered'].iloc[-1],
         timeseries['totaldeceased'].iloc[-1]],
    y = ["Total Cases", "Total Recovered",  "Deaths"],
    textposition = "inside",
    textinfo = "value",
    opacity = 0.8, 
    marker = {"color": [confirmed_color,recovered_color,death_color],
              "line": {"width": 2.5, "color": 'Black'}},
    connector = {"line": {"color": "navy", "dash": "dot", "width": 2.5}}))
fig.update_layout(
    template="simple_white",
    title={'text': "COVID19: Pandemic in India",'x':0.5,'y':0.9,       
        'xanchor': 'center','yanchor': 'top'})
fig.update_layout(height=700)
fig.show()

temp = statewise_total[statewise_total['State/UT']!='Total']
temp = temp[temp['State/UT']!='State Unassigned']
fig = go.Figure(data=[
    go.Bar(name='Death', y=temp['State/UT'], x=temp['Death'],orientation='h',marker_color=death_color),
    go.Bar(name='Recovered', y=temp['State/UT'], x=temp['Recovered'],orientation='h',marker_color=recovered_color),
    go.Bar(name='Confirmed', y=temp['State/UT'], x=temp['Confirmed'],orientation='h',marker_color=confirmed_color)
])
fig.update_layout(barmode='stack',title='Statewise Confirmed/Recovered/Death Stacked', xaxis_title="Cases", yaxis_title="State/UT", 
                      yaxis_categoryorder = 'total ascending', height = 1000,
                      template='simple_white')
fig.show()

temp = statewise_total[statewise_total['State/UT']!='Total']
temp = temp[temp['State/UT']!='State Unassigned']
fig = go.Figure()
fig.add_trace(go.Scatter(y=temp['Death'], x=temp['State/UT'],
                    mode='lines+markers',
                    name='Death',marker_color=death_color))
fig.add_trace(go.Scatter(y=temp['Recovered'], x=temp['State/UT'],
                    mode='lines+markers',
                    name='Recovered',marker_color=recovered_color))
fig.add_trace(go.Scatter(y=temp['Active'], x=temp['State/UT'],
                    mode='lines+markers', name='Active',marker_color=active_color))
fig.add_trace(go.Scatter(y=temp['Confirmed'], x=temp['State/UT'],
                    mode='lines+markers', name='Confirmed',marker_color=confirmed_color))
fig.update_layout(height=900,width= 1200, title_text="Statewise Cases",template='simple_white')
fig.show()

temp = statewise_total[statewise_total['State/UT']!='Total']
temp = temp[temp['State/UT']!='State Unassigned']
fig = go.Figure()
fig.add_trace(go.Scatter(y=temp['MortalityRate%'], x=temp['State/UT'],
                    mode='lines+markers',
                    name='Mortality Rate',marker_color=death_color))
fig.add_trace(go.Scatter(y=temp['RecoveryRate%'], x=temp['State/UT'],
                    mode='lines+markers',
                    name='Recovery Rate',marker_color=recovered_color))
fig.add_trace(go.Scatter(y=temp['Active/100 Confirmed'], x=temp['State/UT'],
                    mode='lines+markers', name='Active/100 Confirmed',marker_color=active_color))
fig.update_layout(height=700,width= 1200, title_text="Statewise Cases per 100 Confirmed",template='simple_white')
fig.show()

fig = px.bar(timeseries, x='date', y='totalconfirmed', color_discrete_sequence=[confirmed_color],template='simple_white')
fig.update_layout(title='Confirmed', xaxis_title="Date", yaxis_title="No. of Confirmed Cases")
fig.add_scatter(x=timeseries['date'],y=timeseries['7dyMnConfirmed'],name='7 day mean Confirmed',
                marker={'color': 'red','opacity': 0.6,'colorscale': 'Viridis'},)
fig.show()

fig = px.bar(timeseries, x='date', y='totalrecovered', 
             color_discrete_sequence=[recovered_color],template='simple_white')
fig.update_layout(title='Recovered', xaxis_title="Date", yaxis_title="No. of Recovered Cases")
fig.add_scatter(x=timeseries['date'],y=timeseries['7dyMnRecovered'],name='7 day mean Recovered',
                marker={'color': 'red','opacity': 0.6,'colorscale': 'Viridis'},)
fig.show()

fig = px.bar(timeseries, x='date', y='totaldeceased', 
             color_discrete_sequence=[death_color],template='simple_white')
fig.update_layout(title='Death', xaxis_title="Date", yaxis_title="No. of Deceased Cases")
fig.add_scatter(x=timeseries['date'],y=timeseries['7dyMnDeceased'],name='7 day mean Deceased',
                marker={'color': 'black','opacity': 0.6,'colorscale': 'Viridis'},)
fig.show()

timeseries['totalactive'] = timeseries.totalconfirmed-timeseries.totalrecovered-timeseries.totaldeceased
fig = px.line(color_discrete_sequence=[confirmed_color],template='simple_white')
fig.add_scatter(x=timeseries['date'],y=timeseries['totalconfirmed'],name='Total Confirmed',marker={'color': confirmed_color,'opacity': 0.6,'colorscale': 'Viridis'},)
fig.add_scatter(x=timeseries['date'],y=timeseries['totalrecovered'],name='Total Recovered',marker={'color': recovered_color,'opacity': 0.6,'colorscale': 'Viridis'},)
fig.add_scatter(x=timeseries['date'],y=timeseries['totaldeceased'],name='Total Death',marker={'color': death_color,'opacity': 0.6,'colorscale': 'Viridis'})
fig.add_scatter(x=timeseries['date'],y=timeseries['totalactive'],name='Total Active',marker={'color': active_color,'opacity': 0.6,'colorscale': 'Viridis'})
fig.update_layout(title='Total Cases', xaxis_title="Date", yaxis_title="No. of Cases")
fig.show()

temp = district_wise.sort_values('Confirmed').tail(21)
fig = go.Figure(data=[
    go.Bar(name='Death', y=temp['District'], x=temp['Death'].head(21),orientation='h',marker_color=death_color),
    go.Bar(name='Recovered', y=temp['District'], x=temp['Recovered'].head(21),orientation='h',marker_color=recovered_color),
    go.Bar(name='Confirmed', y=temp['District'], x=temp['Confirmed'].head(21),orientation='h',marker_color=confirmed_color)
])
fig.update_layout(barmode='stack',title='Top21 Confirmed/Recovered/Death Stacked', xaxis_title="Cases", yaxis_title="District", 
                      yaxis_categoryorder = 'total ascending',
                     template='simple_white')
fig.show() 

Unknown is Delhi.

district_wise = district_wise[district_wise['State/UT']!='State Unassigned']
district_wise.sort_values('Confirmed', ascending= False).head(30).fillna(0).style\
                        .background_gradient(cmap='Blues_r',subset=["Confirmed"])\
                        .background_gradient(cmap='Greens_r',subset=["Recovered"])\
                        .background_gradient(cmap='Reds_r',subset=["Death"])\
                        .background_gradient(cmap='pink_r',subset=["Active"])

	State/UT	District	Confirmed	Recovered	Active	Death
160	Delhi	Unknown	1436101	1411327	-293	25067
289	Karnataka	Bengaluru Urban	1231474	1207260	8290	15923
361	Maharashtra	Pune	1099851	1066447	14419	18679
351	Maharashtra	Mumbai	738239	715650	4212	15968
368	Maharashtra	Thane	592025	574998	5901	11092
619	Tamil Nadu	Chennai	540300	529907	2048	8345
353	Maharashtra	Nagpur	493063	483591	264	9137
325	Kerala	Malappuram	440734	410437	28759	1508
318	Kerala	Ernakulam	430396	401576	26921	1830
356	Maharashtra	Nashik	403658	394257	839	8561
324	Kerala	Kozhikode	387573	362390	23271	1869
329	Kerala	Thrissur	355009	341779	11239	1965
328	Kerala	Thiruvananthapuram	343093	330216	9386	3376
766	West Bengal	North 24 Parganas	320162	314358	1213	4591
762	West Bengal	Kolkata	311183	305367	835	4981
335	Maharashtra	Ahmednagar	294758	283222	5231	6304
322	Kerala	Kollam	284264	280404	2412	1389
8	Andhra Pradesh	East Godavari	281384	277087	3065	1232
326	Kerala	Palakkad	269048	249691	17683	1652
711	Uttar Pradesh	Lucknow	238644	235939	54	2651
170	Gujarat	Ahmedabad	238022	234550	61	3411
317	Kerala	Alappuzha	237133	227678	8265	1150
7	Andhra Pradesh	Chittoor	234198	229637	2778	1783
323	Kerala	Kottayam	231995	223230	7989	762
620	Tamil Nadu	Coimbatore	231863	227403	2259	2201
365	Maharashtra	Satara	226151	214414	6239	5471
349	Maharashtra	Kolhapur	199490	189411	4427	5647
320	Kerala	Kannur	199236	189191	8847	1147
367	Maharashtra	Solapur	194066	183777	5207	4986
364	Maharashtra	Sangli	192215	180025	6964	5217